OpenRaiser/Intern-Atlas huggingface dataset:
Fractional Graph Coloring for Functional Compression with Side Information
Venue: arXiv cs.IT 2022
Year: 2022

We describe a rational approach to reduce the computational and communication complexities of lossless point-to-point compression for computation with side information. The traditional method relies on building a characteristic graph with vertices representing the source symbols and with edges that assign a source symbol to a collection of independent sets to be distinguished for the exact recovery of the function. Our approach uses fractional coloring for a b-fold coloring of characteristic graphs to provide a linear programming relaxation to the traditional coloring method and achieves coding at a fine-grained granularity. We derive the fundamental lower bound for compression, given by the fractional characteristic graph entropy, through generalizing the notion of Körner's graph entropy. We demonstrate the coding gains of fractional coloring over traditional coloring via a computation example. We conjecture that the integrality gap between fractional coloring and traditional coloring approaches the smallest b that attains the fractional chromatic number to losslessly represent the independent sets for a given characteristic graph, up to a linear scaling which is a function of the fractional chromatic number.
Byzantine-Resilient Counting in Networks
Venue: arXiv cs.DC 2022
Year: 2022

We present two distributed algorithms for the {\em Byzantine counting problem}, which is concerned with estimating the size of a network in the presence of a large number of Byzantine nodes. In an $n$-node network ($n$ is unknown), our first algorithm, which is {\em deterministic}, finishes in $O(\log{n})$ rounds and is time-optimal. This algorithm can tolerate up to $O(n^{1 - γ})$ arbitrarily (adversarially) placed Byzantine nodes for any arbitrarily small (but fixed) positive constant $γ$. It outputs a (fixed) constant factor estimate of $\log{n}$ that would be known to all but $o(1)$ fraction of the good nodes. This algorithm works for \emph{any} bounded degree expander network. However, this algorithms assumes that good nodes can send arbitrarily large-sized messages in a round. Our second algorithm is {\em randomized} and most good nodes send only small-sized messages (Throughout this paper, a small-sized message is defined to be one that contains $O(\log{n})$ bits in addition to at most a constant number of node IDs.). This algorithm works in \emph{almost all} $d$-regular graphs. It tolerates up to $B(n) = n^{\frac{1}{2} - ξ}$ (note that $n$ and $B(n)$ are unknown to the algorithm) arbitrarily (adversarially) placed Byzantine nodes, where $ξ$ is any arbitrarily small (but fixed) positive constant. This algorithm takes $O(B(n)\log^2{n})$ rounds and outputs a (fixed) constant factor estimate of $\log{n}$ with probability at least $1 - o(1)$. The said estimate is known to most nodes, i.e., $\geq (1 - β)n$ nodes for any arbitrarily small (but fixed) positive constant $β$. To complement our algorithms, we also present an impossibility result that shows that it is impossible to estimate the network size with any reasonable approximation with any non-trivial probability of success if the network does not have sufficient vertex expansion.
Decentralisation Conscious Players And System Reliability
Venue: arXiv cs.GT 2022
Year: 2022

We propose a game-theoretic model of the reliability of decentralised systems based on Varian's model of system reliability, to which we add a new normalised total effort case that models \textit{decentralisation conscious players} who prioritise decentralisation. We derive the Nash equilibria in the normalised total effort game. In these equilibria, either one or two values are played by players that do not free ride. The speed at which players can adjust their contributions can determine how an equilibrium is reached and equilibrium values. The behaviour of decentralisation conscious players is robust to deviations by other players. Our results highlight the role that decentralisation conscious players can play in maintaining decentralisation. They also highlight, however, that by supporting an equilibrium that requires an important contribution they cannot be expected to increase decentralisation as contributing the equilibrium value may still imply a loss for many players. We also discuss practical constraints on decentralisation in the context of our model.
Contrastive learning-based computational histopathology predict differential expression of cancer driver genes
Venue: arXiv cs.CV 2022
Year: 2022

Digital pathological analysis is run as the main examination used for cancer diagnosis. Recently, deep learning-driven feature extraction from pathology images is able to detect genetic variations and tumor environment, but few studies focus on differential gene expression in tumor cells. In this paper, we propose a self-supervised contrastive learning framework, HistCode, to infer differential gene expressions from whole slide images (WSIs). We leveraged contrastive learning on large-scale unannotated WSIs to derive slide-level histopathological feature in latent space, and then transfer it to tumor diagnosis and prediction of differentially expressed cancer driver genes. Our extensive experiments showed that our method outperformed other state-of-the-art models in tumor diagnosis tasks, and also effectively predicted differential gene expressions. Interestingly, we found the higher fold-changed genes can be more precisely predicted. To intuitively illustrate the ability to extract informative features from pathological images, we spatially visualized the WSIs colored by the attentive scores of image tiles. We found that the tumor and necrosis areas were highly consistent with the annotations of experienced pathologists. Moreover, the spatial heatmap generated by lymphocyte-specific gene expression patterns was also consistent with the manually labeled WSI.
Virtual Reality Applications in Software Engineering Education: A Systematic Review
Venue: arXiv cs.SE 2022
Year: 2022

Requirement Engineering (RE) is a Software Engineering (SE) process of defining, documenting, and maintaining the requirements from a problem. It is one of the most complex processes of SE because it addresses the relation between customer and developer. RE learning may be abstract and complex for most students because many of them cannot visualize the subject directly applied. Through the advancement of technology, Virtual Reality (VR) hardware is becoming increasingly more accessible, and it is not rare to use it in education. Little research and systematic studies explain the integration between SE and VR, and even less between RE and VR. Hence, this systematic review proposes to select and present studies that relate the use of VR applications to teach SE and RE concepts. We selected nine studies to include in this review. Despite the lack of articles addressing the topic, the results from this study showed that the use of VR technologies for learning SE is still very seminal. The projects based essentially on visualization. There are lack of tasks to build modeling artifacts, and also interaction with stakeholders and other software engineers. Learning tasks and the monitoring of students' progress by teachers also need to be considered.
BATS: Best Action Trajectory Stitching
Venue: arXiv cs.LG 2022
Year: 2022

The problem of offline reinforcement learning focuses on learning a good policy from a log of environment interactions. Past efforts for developing algorithms in this area have revolved around introducing constraints to online reinforcement learning algorithms to ensure the actions of the learned policy are constrained to the logged data. In this work, we explore an alternative approach by planning on the fixed dataset directly. Specifically, we introduce an algorithm which forms a tabular Markov Decision Process (MDP) over the logged data by adding new transitions to the dataset. We do this by using learned dynamics models to plan short trajectories between states. Since exact value iteration can be performed on this constructed MDP, it becomes easy to identify which trajectories are advantageous to add to the MDP. Crucially, since most transitions in this MDP come from the logged data, trajectories from the MDP can be rolled out for long periods with confidence. We prove that this property allows one to make upper and lower bounds on the value function up to appropriate distance metrics. Finally, we demonstrate empirically how algorithms that uniformly constrain the learned policy to the entire dataset can result in unwanted behavior, and we show an example in which simply behavior cloning the optimal policy of the MDP created by our algorithm avoids this problem.
A construction of optimal locally recoverable codes
Venue: arXiv cs.IT 2022
Year: 2022

Locally recoverable codes are widely used in distributed and cloud storage systems. The objective of this paper is to present a construction of near MDS codes with oval polynomials and then determine the locality of the codes. It turns out that the near MDS codes and their duals are both distance-optimal and dimension-optimal locally recoverable codes. The lengths of the locally recoverable codes are different from known ones in the literature.
Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks
Venue: arXiv cs.CV 2022
Year: 2022

Malicious intelligent algorithms greatly threaten the security of social users' privacy by detecting and analyzing the uploaded photos to social network platforms. The destruction to DNNs brought by the adversarial attack sparks the potential that adversarial examples serve as a new protection mechanism for privacy security in social networks. However, the existing adversarial example does not have recoverability for serving as an effective protection mechanism. To address this issue, we propose a recoverable generative adversarial network to generate self-recoverable adversarial examples. By modeling the adversarial attack and recovery as a united task, our method can minimize the error of the recovered examples while maximizing the attack ability, resulting in better recoverability of adversarial examples. To further boost the recoverability of these examples, we exploit a dimension reducer to optimize the distribution of adversarial perturbation. The experimental results prove that the adversarial examples generated by the proposed method present superior recoverability, attack ability, and robustness on different datasets and network architectures, which ensure its effectiveness as a protection mechanism in social networks.
Suggesting Relevant Questions for a Query Using Statistical Natural Language Processing Technique
Venue: arXiv cs.CL 2022
Year: 2022

Suggesting similar questions for a user query has many applications ranging from reducing search time of users on e-commerce websites, training of employees in companies to holistic learning for students. The use of Natural Language Processing techniques for suggesting similar questions is prevalent over the existing architecture. Mainly two approaches are studied for finding text similarity namely syntactic and semantic, however each has its draw-backs and fail to provide the desired outcome. In this article, a self-learning combined approach is proposed for determining textual similarity that introduces a robust weighted syntactic and semantic similarity index for determining similar questions from a predetermined database, this approach learns the optimal combination of the mentioned approaches for a database under consideration. Comprehensive analysis has been carried out to justify the efficiency and efficacy of the proposed approach over the existing literature.
Approach to Predicting News -- A Precise Multi-LSTM Network With BERT
Venue: arXiv cs.CL 2022
Year: 2022

Varieties of Democracy (V-Dem) is a new approach to conceptualizing and measuring democracy and politics. It has information for 200 countries and is one of the biggest databases for political science. According to the V-Dem annual democracy report 2019, Taiwan is one of the two countries that got disseminated false information from foreign governments the most. It also shows that the "made-up news" has caused a great deal of confusion in Taiwanese society and has serious impacts on global stability. Although there are several applications helping distinguish the false information, we found out that the pre-processing of categorizing the news is still done by human labor. However, human labor may cause mistakes and cannot work for a long time. The growing demands for automatic machines in the near decades show that while the machine can do as good as humans or even better, using machines can reduce humans' burden and cut down costs. Therefore, in this work, we build a predictive model to classify the category of news. The corpora we used contains 28358 news and 200 news scraped from the online newspaper Liberty Times Net (LTN) website and includes 8 categories: Technology, Entertainment, Fashion, Politics, Sports, International, Finance, and Health. At first, we use Bidirectional Encoder Representations from Transformers (BERT) for word embeddings which transform each Chinese character into a (1,768) vector. Then, we use a Long Short-Term Memory (LSTM) layer to transform word embeddings into sentence embeddings and add another LSTM layer to transform them into document embeddings. Each document embedding is an input for the final predicting model, which contains two Dense layers and one Activation layer. And each document embedding is transformed into 1 vector with 8 real numbers, then the highest one will correspond to the 8 news categories with up to 99% accuracy.
Centimeter-level Positioning by Instantaneous Lidar-aided GNSS Ambiguity Resolution
Venue: arXiv cs.RO 2022
Year: 2022

High-precision vehicle positioning is key to the implementation of modern driving systems in urban environments. Global Navigation Satellite System (GNSS) carrier phase measurements can provide millimeter- to centimeter-level positioning, provided that the integer ambiguities are correctly resolved. Abundant code measurements are often used to facilitate integer ambiguity resolution (IAR), however, they suffer from signal blockage and multipath in urban canyons. In this contribution, a lidar-aided instantaneous ambiguity resolution method is proposed. Lidar measurements, in the form of 3D keypoints, are generated by a learning-based point cloud registration method using a pre-built HD map and integrated with GNSS observations in a mixed measurement model to produce precise float solutions, which in turn increase the ambiguity success rate. Closed-form expressions of the ambiguity variance matrix and the associated Ambiguity Dilution of Precision (ADOP) are developed to provide a priori evaluation of such lidar-aided ambiguity resolution performance. Both analytical and experimental results show that the proposed method enables successful instantaneous IAR with limited GNSS satellites and frequencies, leading to centimeter-level vehicle positioning.
Deeper Insights into the Robustness of ViTs towards Common Corruptions
Venue: arXiv cs.CV 2022
Year: 2022

With Vision Transformers (ViTs) making great advances in a variety of computer vision tasks, recent literature have proposed various variants of vanilla ViTs to achieve better efficiency and efficacy. However, it remains unclear how their unique architecture impact robustness towards common corruptions. In this paper, we make the first attempt to probe into the robustness gap among ViT variants and explore underlying designs that are essential for robustness. Through an extensive and rigorous benchmarking, we demonstrate that simple architecture designs such as overlapping patch embedding and convolutional feed-forward network (FFN) can promote the robustness of ViTs. Moreover, since training ViTs relies heavily on data augmentation, whether previous CNN-based augmentation strategies that are targeted at robustness purposes can still be useful is worth investigating. We explore different data augmentation on ViTs and verify that adversarial noise training is powerful while fourier-domain augmentation is inferior. Based on these findings, we introduce a novel conditional method of generating dynamic augmentation parameters conditioned on input images, offering state-of-the-art robustness towards common corruptions.
Budgeted Out-tree Maximization with Submodular Prizes
Venue: arXiv cs.DS 2022
Year: 2022

We consider a variant of the prize collecting Steiner tree problem in which we are given a \emph{directed graph} $D=(V,A)$, a monotone submodular prize function $p:2^V \rightarrow \mathbb{R}^+ \cup \{0\}$, a cost function $c:V \rightarrow \mathbb{Z}^{+}$, a root vertex $r \in V$, and a budget $B$. The aim is to find an out-subtree $T$ of $D$ rooted at $r$ that costs at most $B$ and maximizes the prize function. We call this problem \emph{Directed Rooted Submodular Tree} (\textbf{DRSO}). Very recently, Ghuge and Nagarajan [SODA\ 2020] gave an optimal quasi-polynomial-time $O\left(\frac{\log n'}{\log \log n'}\right)$-approximation algorithm, where $n'$ is the number of vertices in an optimal solution, for the case in which the costs are associated to the edges. In this paper, we give a polynomial-time algorithm for \textbf{DRSO} that guarantees an approximation factor of $O(\sqrt{B}/ε^3)$ at the cost of a budget violation of a factor $1+ε$, for any $ε\in (0,1]$. The same result holds for the edge-cost case, to the best of our knowledge this is the first polynomial-time approximation algorithm for this case. We further show that the unrooted version of \textbf{DRSO} can be approximated to a factor of $O(\sqrt{B})$ without budget violation, which is an improvement over the factor $O(Δ\sqrt{B})$ given in~[Kuo et al.\ IEEE/ACM\ Trans.\ Netw.\ 2015] for the undirected and unrooted case, where $Δ$ is the maximum degree of the graph. Finally, we provide some new/improved approximation bounds for several related problems, including the additive-prize version of \textbf{DRSO}, the maximum budgeted connected set cover problem, and the budgeted sensor cover problem.
Understanding User Satisfaction with Task-oriented Dialogue Systems
Venue: arXiv cs.IR 2022
Year: 2022

$ $Dialogue systems are evaluated depending on their type and purpose. Two categories are often distinguished: (1) task-oriented dialogue systems (TDS), which are typically evaluated on utility, i.e., their ability to complete a specified task, and (2) open domain chatbots, which are evaluated on the user experience, i.e., based on their ability to engage a person. What is the influence of user experience on the user satisfaction rating of TDS as opposed to, or in addition to, utility? We collect data by providing an additional annotation layer for dialogues sampled from the ReDial dataset, a widely used conversational recommendation dataset. Unlike prior work, we annotate the sampled dialogues at both the turn and dialogue level on six dialogue aspects: relevance, interestingness, understanding, task completion, efficiency, and interest arousal. The annotations allow us to study how different dialogue aspects influence user satisfaction. We introduce a comprehensive set of user experience aspects derived from the annotators' open comments that can influence users' overall impression. We find that the concept of satisfaction varies across annotators and dialogues, and show that a relevant turn is significant for some annotators, while for others, an interesting turn is all they need. Our analysis indicates that the proposed user experience aspects provide a fine-grained analysis of user satisfaction that is not captured by a monolithic overall human rating.
Open or not open: Are conventional radio access networks more secure and trustworthy than Open-RAN?
Venue: arXiv cs.CR 2022
Year: 2022

The Open RAN architecture is a promising and future-oriented architecture. It is intended to open up the radio access network (RAN) and enable more innovation and competition in the market. This will lead to RANs for current 5G networks, but especially for future 6G networks, evolving from the current highly integrated, vendor-specific RAN architecture towards disaggregated architectures with open interfaces that will enable to better tailor RAN solutions to the requirements of 5G and 6G applications. However, the introduction of such an open architecture substantially broadens the attack possibilities when compared to conventional RANs. In the past, this has often led to negative headlines that in summary have associated Open RAN with faulty or inadequate security. In this paper, we analyze what components are involved in an Open RAN deployment, how to assess the current state of security, and what measures need to be taken to ensure secure operation.
Hybridised Loss Functions for Improved Neural Network Generalisation
Venue: arXiv cs.LG 2022
Year: 2022

Loss functions play an important role in the training of artificial neural networks (ANNs), and can affect the generalisation ability of the ANN model, among other properties. Specifically, it has been shown that the cross entropy and sum squared error loss functions result in different training dynamics, and exhibit different properties that are complementary to one another. It has previously been suggested that a hybrid of the entropy and sum squared error loss functions could combine the advantages of the two functions, while limiting their disadvantages. The effectiveness of such hybrid loss functions is investigated in this study. It is shown that hybridisation of the two loss functions improves the generalisation ability of the ANNs on all problems considered. The hybrid loss function that starts training with the sum squared error loss function and later switches to the cross entropy error loss function is shown to either perform the best on average, or to not be significantly different than the best loss function tested for all problems considered. This study shows that the minima discovered by the sum squared error loss function can be further exploited by switching to cross entropy error loss function. It can thus be concluded that hybridisation of the two loss functions could lead to better performance in ANNs.